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1. Aerosol composition, air quality, and boundary layer dynamics in the urban background of Stuttgart in winter

Author

H. Zhang, W. Huang, X. Shen, R. Ramisetty, J. Song, O. Kiseleva, C. C. Holst, B. Khan, T. Leisner, and H. Saathoff

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Aerosol distributions are of great relevance for air quality, especially for cities like Stuttgart, which has limited air exchange due to its location in a basin. We collected a comprehensive set of data from remote sensing and in situ methods including radiosondes for the urban background of downtown Stuttgart to determine the impact of boundary layer mixing processes on local air quality and to evaluate the simulation results of the high-resolution large eddy simulation (LES) model PALM-4U at 10 m grid spacing. Stagnant meteorological conditions caused accumulation of aerosols, and chemical composition analysis shows that ammonium nitrate (37 ± 9 %) and organic aerosol (OA; 34 ± 9 %) dominated during this winter study. Case studies show that clouds during previous nights can weaken temperature inversion and accelerate boundary layer mixing after sunrise by up to 3 h. This is important for ground-level aerosol dilution during the morning rush hour. Furthermore, our observations validate results of the LES model PALM-4U in terms of boundary layer heights and aerosol mixing for 48 h. The simulated aerosol concentrations follow the trend of our observations but are still underestimated by a factor of 4.5 ± 2.1 due to missing secondary aerosol formation processes and uncertainties of emissions and boundary conditions in the model. This paper firstly evaluates the PALM-4U model performance in simulating aerosol spatio-temporal distributions, which can help to improve the LES model and to better understand sources and sinks for air pollution as well as the role of horizontal and vertical transport.

Published
2024
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2. GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system

Author

D. Lin, J. Zhang, B. Khan, M. Katurji, and L. E. Revell

Subjects
Geology, QE1-996.5
Abstract

A geospatial data processing tool, GEO4PALM, has been developed to generate geospatial static input for the Parallelized Large-Eddy Simulation (PALM) model system. PALM is a community-driven large-eddy simulation model for atmospheric and environmental research. Throughout PALM's 20-year development, research interests have been increasing in its application to realistic conditions, especially for urban areas. For such applications, geospatial static input is essential. Although abundant geospatial data are accessible worldwide, geospatial data availability and quality are highly variable and inconsistent. Currently, the geospatial static input generation tools in the PALM community heavily rely on users for data acquisition and pre-processing. New PALM users face large obstacles, including significant time commitments, to gain the knowledge needed to be able to pre-process geospatial data for PALM. Expertise beyond atmospheric and environmental research is frequently needed to understand the data sets required by PALM. Here, we present GEO4PALM, which is a free and open-source tool. GEO4PALM helps users generate PALM static input files with a simple, homogenised, and standardised process. GEO4PALM is compatible with geospatial data obtained from any source, provided that the data sets comply with standard geo-information formats. Users can either provide existing geospatial data sets or use the embedded data interfaces to download geo-information data from free online sources for any global geographic area of interest. All online data sets incorporated in GEO4PALM are globally available, with several data sets having the finest resolution of 1 m. In addition, GEO4PALM provides a graphical user interface (GUI) for PALM domain configuration and visualisation. Two application examples demonstrate successful PALM simulations driven by geospatial input generated by GEO4PALM using different geospatial data sources for Berlin, Germany, and Ōtautahi / Christchurch, New Zealand. GEO4PALM provides an easy and efficient way for PALM users to configure and conduct PALM simulations for applications and investigations such as urban heat island effects, air pollution dispersion, renewable energy resourcing, and weather-related hazard forecasting. The wide applicability of GEO4PALM makes PALM more accessible to a wider user base in the scientific community.

Published
2024
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3. Investigating multiscale meteorological controls and impact of soil moisture heterogeneity on radiation fog in complex terrain using semi-idealised simulations

Author

D. Lin, M. Katurji, L. E. Revell, B. Khan, and A. Sturman

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

Coupled surface–atmosphere high-resolution mesoscale simulations were carried out to understand meteorological processes involved in the radiation fog life cycle in a city surrounded by complex terrain. The controls of mesoscale meteorology and microscale soil moisture heterogeneity on fog were investigated using case studies for the city of Ōtautahi / Christchurch, New Zealand. Numerical model simulations from the synoptic to microscale were carried out using the Weather Research and Forecasting (WRF) model and the Parallelised Large-Eddy Simulation Model (PALM). Heterogeneous soil moisture, land use, and topography were included. The spatial heterogeneity of soil moisture was derived using Landsat 8 satellite imagery and ground-based meteorological observations. Nine semi-idealised simulations were carried out under identical meteorological conditions. One contained homogeneous soil moisture of about 0.31 m3 m−3, with two other simulations of halved and doubled soil moisture to demonstrate the range of soil moisture impact. Another contained heterogeneous soil moisture derived from Landsat 8 imagery. For the other five simulations, the soil moisture heterogeneity magnitudes were amplified following the observed spatial distribution to aid our understanding of the impact of soil moisture heterogeneity. Analysis using pseudo-process diagrams and accumulated latent heat flux shows significant spatial heterogeneity of processes involved in the simulated fog. Our results showed that soil moisture heterogeneity did not significantly change the general spatial structure of near-surface fog occurrence, even when the heterogeneity signal was amplified and/or when the soil moisture was halved and doubled. However, compared to homogeneous soil moisture, spatial heterogeneity in soil moisture can lead to changes in fog duration. These changes can be more than 50 min, although they are not directly correlated with spatial variations in soil moisture. The simulations showed that the mesoscale (10 to 200 km) meteorology controls the location of fog occurrence, while soil moisture heterogeneity alters fog duration at the microscale on the order of 100 m to 1 km. Our results highlight the importance of including soil moisture heterogeneity for accurate spatiotemporal fog forecasting.

Published
2023
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4. WRF4PALM v1.0: a mesoscale dynamical driver for the microscale PALM model system 6.0

Author

D. Lin, B. Khan, M. Katurji, L. Bird, R. Faria, and L. E. Revell

Subjects
Geology, QE1-996.5
Abstract

A set of Python-based tools, WRF4PALM, has been developed for offline nesting of the PALM model system 6.0 into the Weather Research and Forecasting (WRF) modelling system. Time-dependent boundary conditions of the atmosphere are critical for accurate representation of microscale meteorological dynamics in high-resolution real-data simulations. WRF4PALM generates initial and boundary conditions from WRF outputs to provide time-varying meteorological forcing for PALM. The WRF model has been used across the atmospheric science community for a broad range of multidisciplinary applications. The PALM model system 6.0 is a turbulence-resolving large-eddy simulation model with an additional Reynolds-averaged Navier–Stokes (RANS) mode for atmospheric and oceanic boundary layer studies at microscale (Maronga et al., 2020). Currently PALM has the capability to ingest output from the regional scale Consortium for Small-scale Modelling (COSMO) atmospheric prediction model. However, COSMO is not an open source model and requires a licence agreement for operational use or academic research (http://www.cosmo-model.org/, last access: 23 April 2021). This paper describes and validates the new free and open-source WRF4PALM tools (available at https://github.com/dongqi-DQ/WRF4PALM, last access: 23 April 2021). Two case studies using WRF4PALM are presented for Christchurch, New Zealand, which demonstrate successful PALM simulations driven by meteorological forcing from WRF outputs. The WRF4PALM tools presented here can potentially be used for micro- and mesoscale studies worldwide, for example in boundary layer studies, air pollution dispersion modelling, wildfire emissions and spread, urban weather forecasting, and agricultural meteorology.

Published
2021
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5. Development of an atmospheric chemistry model coupled to the PALM model system 6.0: implementation and first applications

Author

B. Khan, S. Banzhaf, E. C. Chan, R. Forkel, F. Kanani-Sühring, K. Ketelsen, M. Kurppa, B. Maronga, M. Mauder, S. Raasch, E. Russo, M. Schaap, and M. Sühring

Subjects
Geology, QE1-996.5
Abstract

In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence-resolving PALM model system 6.0 (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name). The new chemistry model is implemented in the PALM model as part of the PALM-4U (PALM for urban applications) components, which are designed for application of the PALM model in the urban environment (Maronga et al., 2020). The latest version of the Kinetic PreProcessor (KPP, 2.2.3) has been utilized for the numerical integration of gas-phase chemical reactions. A number of tropospheric gas-phase chemistry mechanisms of different complexity have been implemented ranging from the photostationary state (PHSTAT) to mechanisms with a strongly simplified volatile organic compound (VOC) chemistry (e.g. the SMOG mechanism from KPP) and the Carbon Bond Mechanism 4 (CBM4; Gery et al., 1989), which includes a more comprehensive, but still simplified VOC chemistry. Further mechanisms can also be easily added by the user. In this work, we provide a detailed description of the chemistry model, its structure and input requirements along with its various features and limitations. A case study is presented to demonstrate the application of the new chemistry model in the urban environment. The computation domain of the case study comprises part of Berlin, Germany. Emissions are considered using street-type-dependent emission factors from traffic sources. Three chemical mechanisms of varying complexity and one no-reaction (passive) case have been applied, and results are compared with observations from two permanent air quality stations in Berlin that fall within the computation domain. Even though the feedback of the model's aerosol concentrations on meteorology is not yet considered in the current version of the model, the results show the importance of online photochemistry and dispersion of air pollutants in the urban boundary layer for high spatial and temporal resolutions. The simulated NOx and O3 species show reasonable agreement with observations. The agreement is better during midday and poorest during the evening transition hours and at night. The CBM4 and SMOG mechanisms show better agreement with observations than the steady-state PHSTAT mechanism.

Published
2021
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6. Multi-Renewable Energy Agent Based Control for Economic Dispatch and Frequency Regulation of Autonomous Renewable Grid

Author

F. Mehmood, B. Khan, S. M. Ali, M. B. Qureshi, C. Diver, and R. Nawaz

Subjects
Distributed SMC, secondary control, frequency regulation, distributed generation, renewable grid, system stability, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper addresses frequency regulation and the economic dispatch problem of an Autonomous Renewable Grid (ARG) primarily composed of Multi Renewable Energy Agents (MRAs), interfaced through DC/AC inverters. A large number of MRAs that have an inherent fluctuating nature and frequent disturbances in inter-connected systems require fast and robust control to stabilize the frequency and to maintain cost-effective operation of an ARG. To address the above control challenges, Distributed Averaging Integrator (DAI) based control schemes were proposed in various research works. The main flaws of such schemes were slow convergence, sluggish response, poor transient performance and a difficult selection of an appropriate damping-factor. The proposed approach introduces a Distributed Sliding Mode Control (DSMC) based solution for fast convergence and improved transient response. The DSMC control is based on a distributed sliding surface, designed using a combination of local information and information from neighboring MRAs. The control is implemented locally at each MRA and achieves the asymptotic global consensus. Finally, the convergence of the proposed control scheme is proved mathematically, and performance is validated using the MRA system which has been implemented using MATLAB/Simulink. The results of the proposed control technique are compared with conventional DAI control, which shows that the proposed scheme outperforms the conventional scheme in terms of fast convergence, considering renewable resources as distributed generation.

Published
2020
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7. An Adaptive Distributed Averaging Integral Control Scheme for Micro-Grids With Renewable Intermittency and Varying Operating Cost

Author

M. A. Rustam, B. Khan, S. M. Ali, M. B. Qureshi, C. A. Mehmood, M. U. S. Khan, and R. Nawaz

Subjects
Adaptive control, distributed averaging based integral, economic load dispatch, micro-grids, renewable intermittency, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The increasing penetration of intermittent renewable energy resources in micro-grids poses several issues, such as stochastic power generation, demand and supply miss-match, frequency fluctuation, and economic dispatch problems. To address such critical issues, a distributed secondary control scheme based for micro-grids with varying operating cost and intermittent renewable energy resources is proposed for frequency regulation and economic load dispatch. The paper presents an adaptive distributed averaging integral control scheme with conditional uncertainties, namely varying operating costs, and renewable intermittency. The proposed control scheme adapts to the uncertainties by updating the control law parameters dynamically and can maintain overall network stability. The distributed control scheme employs communication channels for exchange of generation data from the neighboring power units for optimal power sharing and consensus among the power units. An additional controller at tertiary control layer of the hierarchical control architecture is also augmented in the control structure to economically dispatch the load and the consensus-based algorithm guarantees optimal load sharing. The proposed communication based control scheme reveals the best combination of performance and flexibility. A performance-based comparative analysis is also presented, validating the effectiveness of the proposed control scheme compared to the prior works. The robustness and performance of the proposed control scheme is illustrated through computer simulations.

Published
2020
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8. Anti-hyperglycemic and anti-hyperlipidemic effects of a methanolic extract of Debregeasia salicifolia in Alloxan-induced diabetic albino mice

Author

B. Khan, A. Ullah, M.A. Khan, A. Amin, M. Iqbal, S. Khan, M. Ateeq, K. Aman, A. Aziz, M. N. K. Khattak, T. Nadeem, N. Munir, and Q. Ali

Subjects
diabetes mellitus, debregeasia salicifolia, hypoglycemic, hyperglycemic, hyperlipidemic activity, antidiabetic, phytochemicals, insulin, cholesterol, Science, Biology (General), QH301-705.5, Zoology, QL1-991, Botany, QK1-989
Abstract

Abstract Diabetes mellitus (DM), an endocrine syndrome characterized by high blood glucose levels due to abrogated insulin activity. The existing treatments for DM have side effects and varying degrees of efficacy. Therefore, it is paramount that novel approaches be developed to enhance the management of DM. Therapeutic plants have been accredited as having comparatively high efficacy with fewer adverse effects. The current study aims to elucidate the phytochemical profile, anti-hyperlipidemic, and anti-diabetic effects of methanolic extract D. salicifolia (leaves) in Alloxan-induced diabetic mice. Alloxan was injected intraperitoneally (150 mg kg-1, b.w), to induced diabetes in mice. The mice were divided into three groups (n=10). Group 1 (normal control) received normal food and purified water, Group II (diabetic control) received regular feed and clean water and group III (diabetic treated) received a methanolic extract of the plant (300 mg kg-1) for 28 days with a typical diet and clean water throughout the experiment. Blood samples were collected to checked serum glucose and concentration of LDL, TC, TG. The extract demonstrated significant antihyperglycemic activity (P

Published
2021
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10. Diagnostic approaches and potential therapeutic options for coronavirus disease 2019

Author

Z. Khan, D. Ghafoor, A. Khan, D. Ualiyeva, S.A. Khan, H. Bilal, B. Khan, and W. Sajjad

Subjects
2019-nCoV, Coronavirus 2019, drugs, global tally, severe acute respiratory syndrome coronavirus 2, WHO, Infectious and parasitic diseases, RC109-216
Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan city of China in late December 2019 and identified as a novel coronavirus. Due to its contagious nature, the virus spreads rapidly and causes coronavirus disease 2019 (COVID-19). The global tally of COVID-19 was 28 million in early September 2020. The fears and stress associated with SARS-CoV-2 has demolished the socio-economic status worldwide. Researchers are trying to identify treatments, especially antiviral drugs and/or vaccines, that could potentially control the viral spread and manage the ongoing unprecedented global crisis. To date, more than 300 clinical trials have been conducted on various antiviral drugs, and immunomodulators are being evaluated at various stages of COVID-19. This review aims to collect and summarize a list of drugs used to treat COVID-19, including dexamethasone, chloroquine, hydroxychloroquine, lopinavir/ritonavir, favipiravir, remdesivir, tociluzimab, nitazoxanide and ivermectin. However, some of these drugs are not effective and their use has been suspended by WHO.

Published
2020
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11. Evaluation of 1.5mm Lead Shield for Radiological Protection and Comparison of Calculated and Measured Results of Equivalent Dose

Author

M. Alhassan, B. Khan, and J.T. Baraya

Subjects
Lead shield, radiological protection, effectiveness of 1.5mm leadshield, presence of shield, absence of shield, radiographer’s safety., Science
Abstract

Evaluation of radiation protective devices in radiology departments is one of the practices that ensure radiation protection and staff and patients safety in hospitals. A research work to evaluate 1.5mm lead shield used for radiological protection was carried out in Radiological Unit of Sharda Hospital, of Sharda University, India, using 300mA fixed x-ray machine room. The evaluation was done in the x-ray energy (kVp) range between 52- 81 and by using calculative procedure and by direct measurement of the radiation dose rates. The two results were compared. The results shows that, in the absence of the shield, only 11.82% of the radiation exposure was attenuated by the air space before reaching the radiographer’s stand, while in the presence of the shield, 96.50% was attenuated, whereas, for the measured result only 10.17% was attenuated in the absence of the shield and 89.83% was attenuated in the presence of the shield before reaching the radiographer’s stand. The unit of radiation exposure was converted to that of equivalent dose and that of effective dose in order to assess the radiographer’s safety level behind the shield. It was found that, the equivalent/effective dose is as low as to be accepted according to the policy of ALARA (As Low As Reasonably Achievable), and within the NCRP recommended limit. This guaranteed the effectiveness of the lead shield of 1.5mm thickness in the x-ray energy range used in this study. Keywords: Lead shield, radiological protection, effectiveness of 1.5mm leadshield, presence of shield, absence of shield, radiographer’s safety.

Published
2020

12. Fast Performance Computing Model for Smart Distributed Power Systems

Author

Umair Younas, B. Khan, S. M. Ali, and Alfredo Vaccaro

Subjects
Computer engineering. Computer hardware, TK7885-7895, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

Plug-in Electric Vehicles (PEVs) are becoming the more prominent solution compared to fossil fuels cars technology due to its significant role in Greenhouse Gas (GHG) reduction, flexible storage, and ancillary service provision as a Distributed Generation (DG) resource in Vehicle to Grid (V2G) regulation mode. However, large-scale penetration of PEVs and growing demand of energy intensive Data Centers (DCs) brings undesirable higher load peaks in electricity demand hence, impose supply-demand imbalance and threaten the reliability of wholesale and retail power market. In order to overcome the aforementioned challenges, the proposed research considers smart Distributed Power System (DPS) comprising conventional sources, renewable energy, V2G regulation, and flexible storage energy resources. Moreover, price and incentive based Demand Response (DR) programs are implemented to sustain the balance between net demand and available generating resources in the DPS. In addition, we adapted a novel strategy to implement the computational intensive jobs of the proposed DPS model including incoming load profiles, V2G regulation, battery State of Charge (SOC) indication, and fast computation in decision based automated DR algorithm using Fast Performance Computing resources of DCs. In response, DPS provide economical and stable power to DCs under strict power quality constraints. Finally, the improved results are verified using case study of ISO California integrated with hybrid generation.

Published
2017
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13. Premature Coronary Artery Disease and Familial Hypercholesterolemia: Need for Early Diagnosis and Cascade Screening in the Indian Population

Author

N. Setia, I. C. Verma, B. Khan, and A. Arora

Subjects
Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Cardiovascular disease (CVD) is the leading cause of death in India, accounting for 28% of mortality. The average age of onset of CVD is younger (below 55 years) among Indians than in other populations. This may be due to bad lifestyle, genetic factors, or both. Hypertension, smoking, diabetes, and physical inactivity have been identified as modifiable risk factors for heart disease. Hypercholesterolemia is the most common and treatable cause of heart disease. Genetic factors that lead to hypercholesterolemia have not been fully studied in India. Familial Hypercholesterolemia results from mutations in the LDL receptor, ApoB, PCSK9, and ApoE genes. There is an urgent need to screen subjects with premature CAD and their relatives in India for the presence of FH, identify the mutations that lead to high cholesterol, and carry out cascade screening in the at-risk relatives. Those harbouring mutations in the above genes can be treated to lower the cholesterol levels, prevent early CVD, and avoid death. A programme based on these lines has been initiated in Delhi.

Published
2012
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16. Deepfake Audio Detection.

Author

Madeeha B. Khan, Sanjay Goel, Jaswant Katar Anandan, Jersey Zhao, and Ramavath Rakesh Naik

Published
2022

17. LecturePlus: a learner-centered teaching method to promote deep learning

Author

Muhammad Jawad Hashim, Romona Govender, Nadirah Ghenimi, Alexander Kieu, and Moien A. B. Khan

Subjects
Physiology, General Medicine, Education
Abstract

We developed a new structured teaching format, the LecturePlus, to promote deep learning. A LecturePlus session consists of a brief lecture, followed by small-group problem-solving exercises, ending with a closing discussion moderated by the teacher. During the small-group exercises, students were divided into groups of three (triads) and given case-based problems. One faculty tutor supervised the entire session. LecturePlus resulted in improved learning outcomes and was rated highly by medical students.

Published
2023

18. Associations of Urine Biomarkers of Kidney Tubule Health With Incident Hypertension and Longitudinal Blood Pressure Change in Middle-Aged Adults: The CARDIA Study

Author

Muhammad B. Khan, Rebecca Scherzer, Cora E. Lewis, Rakesh Malhotra, Joachim H. Ix, Michael G. Shlipak, and Orlando M. Gutiérrez

Subjects
Internal Medicine
Abstract

Background: Urine biomarkers of kidney tubule injury associate with incident hypertension in older adults with comorbidities, but less is known about these associations in younger adults. Methods: In 1170 participants of the CARDIA study (Coronary Artery Risk Development in Young Adults; mean age, 45 years; 40% Black people; 56% women) without hypertension, cardiovascular disease, or kidney disease at baseline, we examined associations of urine MCP-1 (monocyte chemoattractant protein-1), α1m (alpha-1-microglobulin), KIM-1 (kidney injury molecule-1), EGF (epidermal growth factor), IL (interleukin)-18, YKL-40 (chitinase-3-like protein 1), and UMOD (uromodulin) with incident hypertension (onset of systolic blood pressure [BP] ≥130 mm Hg or diastolic BP ≥80 mm Hg or initiation of hypertension medications) and longitudinal BP change in models adjusted for hypertension risk factors, estimated glomerular filtration rate, and albuminuria. Results: After a median 9.9 (interquartile range, 5.9–10.2) years, 376 participants developed incident hypertension. In demographic-adjusted analyses, higher tertiles of EGF associated with lower risk of incident hypertension in both Black and White participants. After multivariable adjustment, the risk of incident hypertension remained lower in tertile 2 (hazard ratio, 0.70 [95% CI, 0.50–0.97]) and tertile 3 (hazard ratio, 0.58 [0.39–0.85]) of EGF versus tertile 1. In fully adjusted models, participants in EGF tertile 3 had smaller 10-year increases in systolic (−3.4 [95% CI, −6.1 to −0.7] mm Hg) and diastolic BP (−2.6 [95% CI, −4.6 to −0.6] mm Hg) than tertile 1. Other biomarkers showed inconsistent associations with incident hypertension and BP change. Conclusions: In middle-aged adults without hypertension, cardiovascular disease, or kidney disease, higher urine EGF associated with lower risk of incident hypertension and lower 10-year BP elevations.

Published
2023

19. Efeitos anti-hiperglicêmicos e anti-hiperlipidêmicos de um extrato metanólico de Debregeasia salicifolia em camundongos albinos diabéticos induzidos por aloxana

Author

B. Khan, A. Ullah, M.A. Khan, A. Amin, M. Iqbal, S. Khan, M. Ateeq, K. Aman, A. Aziz, M. N. K. Khattak, T. Nadeem, N. Munir, and Q. Ali

Subjects
Blood Glucose, insulin, QH301-705.5, Science, antidiabético, hiperglicêmica e hiperlipidêmica, Diabetes Mellitus, Experimental, Mice, debregeasia salicifolia, atividade hipoglicêmica, Alloxan, Animals, Hypoglycemic Agents, insulina, Biology (General), Hypolipidemic Agents, fitoquímicos, Plant Extracts, antidiabetic, Botany, cholesterol, colesterol, phytochemicals, hypoglycemic, hyperlipidemic activity, QL1-991, QK1-989, diabetes mellitus, General Agricultural and Biological Sciences, hyperglycemic, Zoology, Phytotherapy
Abstract

Diabetes mellitus (DM), an endocrine syndrome characterized by high blood glucose levels due to abrogated insulin activity. The existing treatments for DM have side effects and varying degrees of efficacy. Therefore, it is paramount that novel approaches be developed to enhance the management of DM. Therapeutic plants have been accredited as having comparatively high efficacy with fewer adverse effects. The current study aims to elucidate the phytochemical profile, anti-hyperlipidemic, and anti-diabetic effects of methanolic extract D. salicifolia (leaves) in Alloxan-induced diabetic mice. Alloxan was injected intraperitoneally (150 mg kg-1, b.w), to induced diabetes in mice. The mice were divided into three groups (n=10). Group 1 (normal control) received normal food and purified water, Group II (diabetic control) received regular feed and clean water and group III (diabetic treated) received a methanolic extract of the plant (300 mg kg-1) for 28 days with a typical diet and clean water throughout the experiment. Blood samples were collected to checked serum glucose and concentration of LDL, TC, TG. The extract demonstrated significant antihyperglycemic activity (P

Published
2024

22. Sero-prevalence of hepatitis-c virus among blood donors in northern Pakistan

Author

Shoukat Ali, Basit Ali, Bakht B Khan, Manzar Khan, Sohail Ali, Asif Iqbal, Tamreez Khan, Shafqat Ullah, Mujeeb Ur Rahman, and Maryam Gul

Subjects
General Medicine
Abstract

HCV is a hepatotropic single strained positive sense RNA virus belongs to family Flaviviridae. HCV is worldwide distributed virus affects about 185 million peoples as a result it causes 500,000 deaths per year. In Pakistan 5-6% of individuals are infected with HCV while in blood donors this ratio varies from 1.05-3.24 in different region of Pakistan. In Peshawar sero-prevalence of HCV was 4 percent. This study focuses on the actual status of HCV among blood donors at district Mardan and to compare the findings with previous study and shows the differences. The data were collected in MMC-MTI Mardan from January 2019 to May 2019. Through CMIA (by architect 1000i) methods the quantitative detection of anti-HCV antibodies takes place and 625 samples diagnosed through EIA methods, in which 9 were positive to HCV antibodies. Overall sero-prevalence of HCV positive donors is 1.44% and high prevalence were founded in the male which was (1.45%), rural area (1.95%), illiterate donors (2.08%), married donors (2.20%), donors belong to lower class economic status (2.21%), 1st time donors (1.72%) and age group 38-47 (3.64%) were positive to HCV. The risk factors of HCV transmission are IVD use, unscreened blood transfusion, contaminated surgical instruments and shaving in barber shop. The findings of this study show that the sero-prevalence of HCV increased 0.24% from 2018 among blood donors at district Mardan. It was recommended that more studies required to explore the seroprevalance of HCV in other cities of Pakistan.

Published
2023

23. A quick review on the mechanism and applications of microbial mediated fuel cells

Author

Asif Iqbal, Bakht B Khan, Sawaira Iqbal, Naib Khan, Muhammad IK Rehman, and Matiullah .

Abstract

A Microbial Fuel Cell (MFC) is a bio electrochemical device that can generate electricity by the use of electrons obtained from the anaerobic oxidation of substrates. Generally, the MFC consists of two parts, an anode and a cathode, which are separated by a Proton Exchange Membrane (PEM). This review was investigated to study MFC and its operational physiochemical, microbial and bioelectrical parameters. Microbial electrochemical technologies constitute a relatively recent innovative approach for wastewater treatment where a laboratory-scale level shows remarkable results in terms of the removal of organic matter and other pollutants of interest, as well as for the recovery of potential energy store in chemical form in wastewaters. It is of interest to invest efforts in systems that take advantage of bio electrochemical principles without simultaneous power generation. An option for this would be to invest higher efforts on the research of sewage operating with MFCs setups, systems operating in short-circuit mode.

Published
2023

24. Comparative study of C-reactive protein and complete blood count in cancer and non-cancer patients followed by antibiogram analysis of isolated bacterial pathogens

Author

Asif Iqbal, Dr. Riaz Muhammad, Bakht B Khan, Aleesha Jamshed, Muhammad IK Rehman, and Sawaira Iqbal

Abstract

Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. This study focuses on the analysis of blood cell and their proteins followed by antibiotic susceptibility and ESBL detection in Cancer and non-Cancer patients. In this study, a total 200 blood and urine samples were screened out for bacteria especially ESBL producing bacteria. Out of the tested samples, different bacterial pathogens were identified and among the isolates E coli were (13.33%), S. aureus (11.66%), P. aeruginosa (11.66%), salmonella (10%), bacillus spps (9.16%), Enterobacter spps (8.33%), Mycobacterium Spps (7.5%), S. Pyogene (7.5%), H. pylori (6.66%), Klebsiella spp. (5.83%), S. epidermidis (4.16 %) and Shigella was (4.16%). The ESBL producing bacteria among the isolated 120 bacterial species were only 14 bacterial isolates are ESBL producers which are E. coli (06), P. aeruginosa (04). Enterobacter spp (04), Klebsiella spp (03) and Shigella were only (01). The 100 blood samples are also analysis through CRP test in which 50 blood samples taken from cancer patients and 50 from non-cancer patients. In total 50 cancer sample CRP analysed that 21 patents have the Lowest (0.92 mg/L), 08 was (0.92-1.93 mg/L) 11 was (1.94-3.69 mg/L) and10 patients were on the Highest (>3.69 mg/L) risk. In 50 non-cancer blood sample the CRP level are 09 individual were (0.92 mg/L), 11 individual (0.92-1.93 mg/L), 25 individual (1.94-3.69 mg/L) and 03 patients were Highest (>3.69 mg/L) risk. Maximum resistivity (91%) was showed by penillion (P) and maximum sensitivity (78%) was showed by rifampicin (RD) against isolated bacterial pathogens. It is recommended that Continuous ESBL screening and supervision are necessary at hospital settings to observe and develop approaches for observing and controlling the spread of ESBL generating bacteria.

Published
2023

25. Social Media and Dentistry

Author

A Davids, A Rawoot, A Sayed, S Bruintjies, K Kajee, A Raabe, and Saadika B. Khan

Subjects
Geriatrics and Gerontology
Abstract

Numerous social media platforms are accessible to healthcare professionals and to patients. The aim of this study was to determine the role of social media platforms in the academic life of undergraduate and postgraduate dental students, and general dentists and specialists working in an academic setting. A cross-sectional survey was conducted targeting 4th and 5th year dentistry students, postgraduate clinical assistants, qualified dentists, and specialists working at an Oral Health Centre. The survey questions focused on the role of social media sites in the academic setting, and how these can be used to interact on a professional basis in sharing knowledge efficiently and for teaching, as a marketing tool and the ethics related to its use. Most participants appreciated the use of social media to share and receive information for educational purposes. They indicated that online communication increases the spread of information and knowledge efficiently and timeously. They also specified taking advantage of this efficient spread amongst the population as a marketing tool to gain patients. Though there are some individuals who do not quite agree and have suspicions for ethical or personal reasons, and they explained this by indicating that once something is posted online it cannot be removed. The study concluded that the use of social media in dentistry has positive and negative aspects, thus the hesitancy to use it and suspicions expressed by participants. Information placed online should be closely monitored even after having received permission to do so.

Published
2022

28. A Training Exoskeleton for Rehabilitation in Upper Extremity for Stroke Patients

Author

Sohaib Faiz, Asim Waris, Shafaq Mushtaq, Niaz B. Khan, Mohammed Jameel, and Ahmed M. Galal

Subjects
Computational Theory and Mathematics, Physical and Theoretical Chemistry, Computer Science Applications
Abstract

With the aim of recovery and training of the patients suffering from osteoarthritis, muscular dystrophy and stroke, we present a design and validation model of a mechanical rehabilitation device for the hand. The objective is to bring together the advances in mechanism effectiveness, reduced size, simpler assembly, and lower manufacturing costs. As a result, the proposed exoskeleton employs a minimal number of components and has a very simplistic design. Moreover, training on the designed hand should reduce spasticity, paresis and recover the tone of the muscle. The designed device can deliver motions that include opening/closing, pronation/supination of the hand and flexion/extension of the hand as well as the arm. This work will focus toward increasing DOF, cost-effectiveness and some modifications in the design to ease assembly. Three servo motors and a linear actuator were used virtually to obtain four different motions. 3D designing of the parts, parts assembly, simulation and studies like kinematic, dynamic and static were carried out. Static analysis of the device shows the device is able to carry the loads without any fracture or deformation. Preliminary results obtained through motion curves show this device is able to deliver all the required motions smoothly without facing any dead point.

Published
2022

29. Fusion of Graph and Tabular Deep Learning Models for Predicting Chronic Kidney Disease

Author

Alharbi, Patike Kiran Rao, Subarna Chatterjee, K Nagaraju, Surbhi B. Khan, Ahlam Almusharraf, and Abdullah I.

Subjects
chronic kidney disease, graph neural network model, GNN, tabular data model, deep learning, prediction, healthcare, CKD
Abstract

Chronic Kidney Disease (CKD) represents a considerable global health challenge, emphasizing the need for precise and prompt prediction of disease progression to enable early intervention and enhance patient outcomes. As per this study, we introduce an innovative fusion deep learning model that combines a Graph Neural Network (GNN) and a tabular data model for predicting CKD progression by capitalizing on the strengths of both graph-structured and tabular data representations. The GNN model processes graph-structured data, uncovering intricate relationships between patients and their medical conditions, while the tabular data model adeptly manages patient-specific features within a conventional data format. An extensive comparison of the fusion model, GNN model, tabular data model, and a baseline model was conducted utilizing various evaluation metrics, encompassing accuracy, precision, recall, and F1-score. The fusion model exhibited outstanding performance across all metrics, underlining its augmented capacity for predicting CKD progression. The GNN model’s performance closely trailed the fusion model, accentuating the advantages of integrating graph-structured data into the prediction process. Hyperparameter optimization was performed using grid search, ensuring a fair comparison among the models. The fusion model displayed consistent performance across diverse data splits, demonstrating its adaptability to dataset variations and resilience against noise and outliers. In conclusion, the proposed fusion deep learning model, which amalgamates the capabilities of both the GNN model and the tabular data model, substantially surpasses the individual models and the baseline model in predicting CKD progression. This pioneering approach provides a more precise and dependable method for early detection and management of CKD, highlighting its potential to advance the domain of precision medicine and elevate patient care.

Published
2023
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30. Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction

Author

Elsisi, Mareeswari Venkatachala Appa Swamy, Jayalakshmi Periyasamy, Muthamilselvan Thangavel, Surbhi B. Khan, Ahlam Almusharraf, Prasanna Santhanam, Vijayan Ramaraj, and Mahmoud

Subjects
IoT, AI, ML, DL, ensemble, GPU, Big Data, contrast agent, diagnostic model
Abstract

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.

Published
2023
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31. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019

Author

Nichols, E, D Steinmetz, J, Emil Vollset, S, Fukutaki, K, Chalek, J, Abd-Allah, F, Abdoli, A, Abualhasan, A, Abu-Gharbieh, E, Tayyaba Akram, T, Al Hamad, H, Alahdab, F, Mashhour Alanezi, F, Alipour, V, Almustanyir, S, Amu, H, Ansari, I, Arabloo, J, Ashraf, T, Astell-Burt, T, Ayano, G, L Ayuso-Mateos, J, Amin Baig, A, Barnett, A, Barrow, A, T Baune, B, Béjot, Y, M Mequanint Bezabhe, W, Mequanint Bezabih, Y, Srikanth Bhagavathula, A, Bhaskar, S, Bhattacharyya, K, Bijani, A, Biswas, A, Rao Bolla, S, Boloor, A, Brayne, C, Brenner, H, Burkart, K, A Burns, R, Alberto Cámera, L, Cao, C, Carvalho, F, S Castro-de-Araujo, L, Catalá-López, F, Cerin, E, P Chavan, P, Cherbuin, N, Chu, D, Marisa Costa, V, S Couto, R, Dadras, O, Dai, X, Dandona, L, Dandona, R, De la Cruz-Góngora, V, Dhamnetiya, D, Dias da Silva, D, Diaz, D, Douiri, A, Edvardsson, D, Ekholuenetale, M, El Sayed, I, I El-Jaafary, S, Eskandari, K, Eskandarieh, S, Esmaeilnejad, S, Fares, J, Faro, A, Farooque, U, L Feigin, V, Feng, X, Fereshtehnejad, S, Fernandes, E, Ferrara, P, Filip, I, Fillit, H, Fischer, F, Gaidhane, S, Galluzzo, L, Ghashghaee, A, Ghith, N, Gialluisi, A, Amir Gilani, S, Glăvan, I, V Gnedovskaya, E, Golechha, M, Gupta, R, Bala Gupta, V, Kumar Gupta, V, Rifat Haider, M, J Hall, B, Hamidi, S, Hanif, A, J Hankey, G, Haque, S, Kusuma Hartono, R, I Hasaballah, A, Tasdik Hasan, M, Hassan, A, I Hay, S, Hayat, K, I Hegazy, M, Heidari, G, Heidari-Soureshjani, R, Herteliu, C, Househ, M, Hussain, R, Hwang, B, Iacoviello, L, Iavicoli, I, Stephen Ilesanmi, O, M Ilic, I, D Ilic, M, Sina Naghibi Irvani, S, Iso, H, Iwagami, M, Jabbarinejad, R, Jacob, L, Jain, V, Kumar Jayapal, S, Jayawardena, R, Prakash Jha, R, B Jonas, J, Joseph, N, Kalani, R, Kandel, A, Kandel, H, Karch, A, Semachew Kasa, A, M Kassie, G, Keshavarz, P, B Khan, M, Nazli Khatib, M, Ahmed Muthafer Khoja, T, Khubchandani, J, Seo Kim, M, Jin Kim, Y, Kisa, A, Kisa, S, Kivimäki, M, J Koroshetz, W, Koyanagi, A, Anil Kumar, G, Kumar, M, Mehmood Lak, H, Leonardi, M, Li, B, S Lim, S, Liu, X, Liu, Y, Logroscino, G, Lorkowski, S, Lucchetti, G, Lutzky Saute, R, Giulia Magnani, F, Azam Malik, A, Massano, J, Mohan Mehndiratta, M, G Menezes, R, Meretoja, A, Mohajer, B, Mohamed Ibrahim, N, Mohammad, Y, Mohammed, A, H Mokdad, A, Mondello, S, Ali Moni, M, Moniruzzaman, M, Belete Mossie, T, Nagel, G, Naveed, M, C Nayak, V, Neupane Kandel, S, Huyen Nguyen, T, Oancea, B, Otstavnov, N, S Otstavnov, S, O Owolabi, M, Panda-Jonas, S, Pashazadeh Kan, F, Pasovic, M, K Patel, U, Pathak, M, P Peres, M, Perianayagam, A, B Peterson, C, R Phillips, M, Pinheiro, M, A Piradov, M, Dimity Pond, C, H Potashman, M, Hyder Pottoo, F, I Prada, S, Radfar, A, Raggi, A, Rahim, F, Rahman, M, Ram, P, Ranasinghe, P, Laith Rawaf, D, Rawaf, S, Rezaei, N, Rezapour, A, R Robinson, S, Romoli, M, Roshandel, G, Sahathevan, R, Sahebkar, A, Ali Sahraian, M, Sathian, B, Sattin, D, Sawhney, M, Saylan, M, Schiavolin, S, Seylani, A, Sha, F, Ali Shaikh, M, S Shaji, K, Shannawaz, M, K Shetty, J, Shigematsu, M, Il Shin, J, Shiri, R, Augusto Santos Silva, D, Pedro Silva, J, Silva, R, A Singh, J, Yurievich Skryabin, V, Aleksandrovna Skryabina, A, E Smith, A, Soshnikov, S, Elizabeth Spurlock, E, J Stein, D, Sun, J, Tabarés-Seisdedos, R, Thakur, B, Timalsina, B, Roberto Tovani-Palone, M, Xuan Tran, B, Wudie Tsegaye, G, Valadan Tahbaz, S, R Valdez, P, Venketasubramanian, N, Vlassov, V, Thu Vu, G, Gia Vu, L, Wang, Y, Wimo, A, Sylvia Winkler, A, Yadav, L, Hossein Yahyazadeh Jabbari, S, Yamagishi, K, Yang, L, Yano, Y, Yonemoto, N, Yu, C, Yunusa, I, Zadey, S, Sergeevich Zastrozhin, M, Zastrozhina, A, Zhang, Z, L Murray, C, Vos, T, Emma Nichols, Jaimie D Steinmetz, Stein Emil Vollset, Kai Fukutaki, Julian Chalek, Foad Abd-Allah, Amir Abdoli, Ahmed Abualhasan, Eman Abu-Gharbieh, Tayyaba Tayyaba Akram, Hanadi Al Hamad, Fares Alahdab, Fahad Mashhour Alanezi, Vahid Alipour, Sami Almustanyir, Hubert Amu, Iman Ansari, Jalal Arabloo, Tahira Ashraf, Thomas Astell-Burt, Getinet Ayano, Jose L Ayuso-Mateos, Atif Amin Baig, Anthony Barnett, Amadou Barrow, Bernhard T Baune, Yannick Béjot, Woldesellassie M Mequanint Bezabhe, Yihienew Mequanint Bezabih, Akshaya Srikanth Bhagavathula, Sonu Bhaskar, Krittika Bhattacharyya, Ali Bijani, Atanu Biswas, Srinivasa Rao Bolla, Archith Boloor, Carol Brayne, Hermann Brenner, Katrin Burkart, Richard A Burns, Luis Alberto Cámera, Chao Cao, Felix Carvalho, Luis F S Castro-de-Araujo, Ferrán Catalá-López, Ester Cerin, Prachi P Chavan, Nicolas Cherbuin, Dinh-Toi Chu, Vera Marisa Costa, Rosa A S Couto, Omid Dadras, Xiaochen Dai, Lalit Dandona, Rakhi Dandona, Vanessa De la Cruz-Góngora, Deepak Dhamnetiya, Diana Dias da Silva, Daniel Diaz, Abdel Douiri, David Edvardsson, Michael Ekholuenetale, Iman El Sayed, Shaimaa I El-Jaafary, Khalil Eskandari, Sharareh Eskandarieh, Saman Esmaeilnejad, Jawad Fares, Andre Faro, Umar Farooque, Valery L Feigin, Xiaoqi Feng, Seyed-Mohammad Fereshtehnejad, Eduarda Fernandes, Pietro Ferrara, Irina Filip, Howard Fillit, Florian Fischer, Shilpa Gaidhane, Lucia Galluzzo, Ahmad Ghashghaee, Nermin Ghith, Alessandro Gialluisi, Syed Amir Gilani, Ionela-Roxana Glăvan, Elena V Gnedovskaya, Mahaveer Golechha, Rajeev Gupta, Veer Bala Gupta, Vivek Kumar Gupta, Mohammad Rifat Haider, Brian J Hall, Samer Hamidi, Asif Hanif, Graeme J Hankey, Shafiul Haque, Risky Kusuma Hartono, Ahmed I Hasaballah, M Tasdik Hasan, Amr Hassan, Simon I Hay, Khezar Hayat, Mohamed I Hegazy, Golnaz Heidari, Reza Heidari-Soureshjani, Claudiu Herteliu, Mowafa Househ, Rabia Hussain, Bing-Fang Hwang, Licia Iacoviello, Ivo Iavicoli, Olayinka Stephen Ilesanmi, Irena M Ilic, Milena D Ilic, Seyed Sina Naghibi Irvani, Hiroyasu Iso, Masao Iwagami, Roxana Jabbarinejad, Louis Jacob, Vardhmaan Jain, Sathish Kumar Jayapal, Ranil Jayawardena, Ravi Prakash Jha, Jost B Jonas, Nitin Joseph, Rizwan Kalani, Amit Kandel, Himal Kandel, André Karch, Ayele Semachew Kasa, Gizat M Kassie, Pedram Keshavarz, Moien A B Khan, Mahalaqua Nazli Khatib, Tawfik Ahmed Muthafer Khoja, Jagdish Khubchandani, Min Seo Kim, Yun Jin Kim, Adnan Kisa, Sezer Kisa, Mika Kivimäki, Walter J Koroshetz, Ai Koyanagi, G Anil Kumar, Manasi Kumar, Hassan Mehmood Lak, Matilde Leonardi, Bingyu Li, Stephen S Lim, Xuefeng Liu, Yuewei Liu, Giancarlo Logroscino, Stefan Lorkowski, Giancarlo Lucchetti, Ricardo Lutzky Saute, Francesca Giulia Magnani, Ahmad Azam Malik, João Massano, Man Mohan Mehndiratta, Ritesh G Menezes, Atte Meretoja, Bahram Mohajer, Norlinah Mohamed Ibrahim, Yousef Mohammad, Arif Mohammed, Ali H Mokdad, Stefania Mondello, Mohammad Ali Moni, Md Moniruzzaman, Tilahun Belete Mossie, Gabriele Nagel, Muhammad Naveed, Vinod C Nayak, Sandhya Neupane Kandel, Trang Huyen Nguyen, Bogdan Oancea, Nikita Otstavnov, Stanislav S Otstavnov, Mayowa O Owolabi, Songhomitra Panda-Jonas, Fatemeh Pashazadeh Kan, Maja Pasovic, Urvish K Patel, Mona Pathak, Mario F P Peres, Arokiasamy Perianayagam, Carrie B Peterson, Michael R Phillips, Marina Pinheiro, Michael A Piradov, Constance Dimity Pond, Michele H Potashman, Faheem Hyder Pottoo, Sergio I Prada, Amir Radfar, Alberto Raggi, Fakher Rahim, Mosiur Rahman, Pradhum Ram, Priyanga Ranasinghe, David Laith Rawaf, Salman Rawaf, Nima Rezaei, Aziz Rezapour, Stephen R Robinson, Michele Romoli, Gholamreza Roshandel, Ramesh Sahathevan, Amirhossein Sahebkar, Mohammad Ali Sahraian, Brijesh Sathian, Davide Sattin, Monika Sawhney, Mete Saylan, Silvia Schiavolin, Allen Seylani, Feng Sha, Masood Ali Shaikh, K S Shaji, Mohammed Shannawaz, Jeevan K Shetty, Mika Shigematsu, Jae Il Shin, Rahman Shiri, Diego Augusto Santos Silva, João Pedro Silva, Renata Silva, Jasvinder A Singh, Valentin Yurievich Skryabin, Anna Aleksandrovna Skryabina, Amanda E Smith, Sergey Soshnikov, Emma Elizabeth Spurlock, Dan J Stein, Jing Sun, Rafael Tabarés-Seisdedos, Bhaskar Thakur, Binod Timalsina, Marcos Roberto Tovani-Palone, Bach Xuan Tran, Gebiyaw Wudie Tsegaye, Sahel Valadan Tahbaz, Pascual R Valdez, Narayanaswamy Venketasubramanian, Vasily Vlassov, Giang Thu Vu, Linh Gia Vu, Yuan-Pang Wang, Anders Wimo, Andrea Sylvia Winkler, Lalit Yadav, Seyed Hossein Yahyazadeh Jabbari, Kazumasa Yamagishi, Lin Yang, Yuichiro Yano, Naohiro Yonemoto, Chuanhua Yu, Ismaeel Yunusa, Siddhesh Zadey, Mikhail Sergeevich Zastrozhin, Anasthasia Zastrozhina, Zhi-Jiang Zhang, Christopher J L Murray, Theo Vos, Nichols, E, D Steinmetz, J, Emil Vollset, S, Fukutaki, K, Chalek, J, Abd-Allah, F, Abdoli, A, Abualhasan, A, Abu-Gharbieh, E, Tayyaba Akram, T, Al Hamad, H, Alahdab, F, Mashhour Alanezi, F, Alipour, V, Almustanyir, S, Amu, H, Ansari, I, Arabloo, J, Ashraf, T, Astell-Burt, T, Ayano, G, L Ayuso-Mateos, J, Amin Baig, A, Barnett, A, Barrow, A, T Baune, B, Béjot, Y, M Mequanint Bezabhe, W, Mequanint Bezabih, Y, Srikanth Bhagavathula, A, Bhaskar, S, Bhattacharyya, K, Bijani, A, Biswas, A, Rao Bolla, S, Boloor, A, Brayne, C, Brenner, H, Burkart, K, A Burns, R, Alberto Cámera, L, Cao, C, Carvalho, F, S Castro-de-Araujo, L, Catalá-López, F, Cerin, E, P Chavan, P, Cherbuin, N, Chu, D, Marisa Costa, V, S Couto, R, Dadras, O, Dai, X, Dandona, L, Dandona, R, De la Cruz-Góngora, V, Dhamnetiya, D, Dias da Silva, D, Diaz, D, Douiri, A, Edvardsson, D, Ekholuenetale, M, El Sayed, I, I El-Jaafary, S, Eskandari, K, Eskandarieh, S, Esmaeilnejad, S, Fares, J, Faro, A, Farooque, U, L Feigin, V, Feng, X, Fereshtehnejad, S, Fernandes, E, Ferrara, P, Filip, I, Fillit, H, Fischer, F, Gaidhane, S, Galluzzo, L, Ghashghaee, A, Ghith, N, Gialluisi, A, Amir Gilani, S, Glăvan, I, V Gnedovskaya, E, Golechha, M, Gupta, R, Bala Gupta, V, Kumar Gupta, V, Rifat Haider, M, J Hall, B, Hamidi, S, Hanif, A, J Hankey, G, Haque, S, Kusuma Hartono, R, I Hasaballah, A, Tasdik Hasan, M, Hassan, A, I Hay, S, Hayat, K, I Hegazy, M, Heidari, G, Heidari-Soureshjani, R, Herteliu, C, Househ, M, Hussain, R, Hwang, B, Iacoviello, L, Iavicoli, I, Stephen Ilesanmi, O, M Ilic, I, D Ilic, M, Sina Naghibi Irvani, S, Iso, H, Iwagami, M, Jabbarinejad, R, Jacob, L, Jain, V, Kumar Jayapal, S, Jayawardena, R, Prakash Jha, R, B Jonas, J, Joseph, N, Kalani, R, Kandel, A, Kandel, H, Karch, A, Semachew Kasa, A, M Kassie, G, Keshavarz, P, B Khan, M, Nazli Khatib, M, Ahmed Muthafer Khoja, T, Khubchandani, J, Seo Kim, M, Jin Kim, Y, Kisa, A, Kisa, S, Kivimäki, M, J Koroshetz, W, Koyanagi, A, Anil Kumar, G, Kumar, M, Mehmood Lak, H, Leonardi, M, Li, B, S Lim, S, Liu, X, Liu, Y, Logroscino, G, Lorkowski, S, Lucchetti, G, Lutzky Saute, R, Giulia Magnani, F, Azam Malik, A, Massano, J, Mohan Mehndiratta, M, G Menezes, R, Meretoja, A, Mohajer, B, Mohamed Ibrahim, N, Mohammad, Y, Mohammed, A, H Mokdad, A, Mondello, S, Ali Moni, M, Moniruzzaman, M, Belete Mossie, T, Nagel, G, Naveed, M, C Nayak, V, Neupane Kandel, S, Huyen Nguyen, T, Oancea, B, Otstavnov, N, S Otstavnov, S, O Owolabi, M, Panda-Jonas, S, Pashazadeh Kan, F, Pasovic, M, K Patel, U, Pathak, M, P Peres, M, Perianayagam, A, B Peterson, C, R Phillips, M, Pinheiro, M, A Piradov, M, Dimity Pond, C, H Potashman, M, Hyder Pottoo, F, I Prada, S, Radfar, A, Raggi, A, Rahim, F, Rahman, M, Ram, P, Ranasinghe, P, Laith Rawaf, D, Rawaf, S, Rezaei, N, Rezapour, A, R Robinson, S, Romoli, M, Roshandel, G, Sahathevan, R, Sahebkar, A, Ali Sahraian, M, Sathian, B, Sattin, D, Sawhney, M, Saylan, M, Schiavolin, S, Seylani, A, Sha, F, Ali Shaikh, M, S Shaji, K, Shannawaz, M, K Shetty, J, Shigematsu, M, Il Shin, J, Shiri, R, Augusto Santos Silva, D, Pedro Silva, J, Silva, R, A Singh, J, Yurievich Skryabin, V, Aleksandrovna Skryabina, A, E Smith, A, Soshnikov, S, Elizabeth Spurlock, E, J Stein, D, Sun, J, Tabarés-Seisdedos, R, Thakur, B, Timalsina, B, Roberto Tovani-Palone, M, Xuan Tran, B, Wudie Tsegaye, G, Valadan Tahbaz, S, R Valdez, P, Venketasubramanian, N, Vlassov, V, Thu Vu, G, Gia Vu, L, Wang, Y, Wimo, A, Sylvia Winkler, A, Yadav, L, Hossein Yahyazadeh Jabbari, S, Yamagishi, K, Yang, L, Yano, Y, Yonemoto, N, Yu, C, Yunusa, I, Zadey, S, Sergeevich Zastrozhin, M, Zastrozhina, A, Zhang, Z, L Murray, C, Vos, T, Emma Nichols, Jaimie D Steinmetz, Stein Emil Vollset, Kai Fukutaki, Julian Chalek, Foad Abd-Allah, Amir Abdoli, Ahmed Abualhasan, Eman Abu-Gharbieh, Tayyaba Tayyaba Akram, Hanadi Al Hamad, Fares Alahdab, Fahad Mashhour Alanezi, Vahid Alipour, Sami Almustanyir, Hubert Amu, Iman Ansari, Jalal Arabloo, Tahira Ashraf, Thomas Astell-Burt, Getinet Ayano, Jose L Ayuso-Mateos, Atif Amin Baig, Anthony Barnett, Amadou Barrow, Bernhard T Baune, Yannick Béjot, Woldesellassie M Mequanint Bezabhe, Yihienew Mequanint Bezabih, Akshaya Srikanth Bhagavathula, Sonu Bhaskar, Krittika Bhattacharyya, Ali Bijani, Atanu Biswas, Srinivasa Rao Bolla, Archith Boloor, Carol Brayne, Hermann Brenner, Katrin Burkart, Richard A Burns, Luis Alberto Cámera, Chao Cao, Felix Carvalho, Luis F S Castro-de-Araujo, Ferrán Catalá-López, Ester Cerin, Prachi P Chavan, Nicolas Cherbuin, Dinh-Toi Chu, Vera Marisa Costa, Rosa A S Couto, Omid Dadras, Xiaochen Dai, Lalit Dandona, Rakhi Dandona, Vanessa De la Cruz-Góngora, Deepak Dhamnetiya, Diana Dias da Silva, Daniel Diaz, Abdel Douiri, David Edvardsson, Michael Ekholuenetale, Iman El Sayed, Shaimaa I El-Jaafary, Khalil Eskandari, Sharareh Eskandarieh, Saman Esmaeilnejad, Jawad Fares, Andre Faro, Umar Farooque, Valery L Feigin, Xiaoqi Feng, Seyed-Mohammad Fereshtehnejad, Eduarda Fernandes, Pietro Ferrara, Irina Filip, Howard Fillit, Florian Fischer, Shilpa Gaidhane, Lucia Galluzzo, Ahmad Ghashghaee, Nermin Ghith, Alessandro Gialluisi, Syed Amir Gilani, Ionela-Roxana Glăvan, Elena V Gnedovskaya, Mahaveer Golechha, Rajeev Gupta, Veer Bala Gupta, Vivek Kumar Gupta, Mohammad Rifat Haider, Brian J Hall, Samer Hamidi, Asif Hanif, Graeme J Hankey, Shafiul Haque, Risky Kusuma Hartono, Ahmed I Hasaballah, M Tasdik Hasan, Amr Hassan, Simon I Hay, Khezar Hayat, Mohamed I Hegazy, Golnaz Heidari, Reza Heidari-Soureshjani, Claudiu Herteliu, Mowafa Househ, Rabia Hussain, Bing-Fang Hwang, Licia Iacoviello, Ivo Iavicoli, Olayinka Stephen Ilesanmi, Irena M Ilic, Milena D Ilic, Seyed Sina Naghibi Irvani, Hiroyasu Iso, Masao Iwagami, Roxana Jabbarinejad, Louis Jacob, Vardhmaan Jain, Sathish Kumar Jayapal, Ranil Jayawardena, Ravi Prakash Jha, Jost B Jonas, Nitin Joseph, Rizwan Kalani, Amit Kandel, Himal Kandel, André Karch, Ayele Semachew Kasa, Gizat M Kassie, Pedram Keshavarz, Moien A B Khan, Mahalaqua Nazli Khatib, Tawfik Ahmed Muthafer Khoja, Jagdish Khubchandani, Min Seo Kim, Yun Jin Kim, Adnan Kisa, Sezer Kisa, Mika Kivimäki, Walter J Koroshetz, Ai Koyanagi, G Anil Kumar, Manasi Kumar, Hassan Mehmood Lak, Matilde Leonardi, Bingyu Li, Stephen S Lim, Xuefeng Liu, Yuewei Liu, Giancarlo Logroscino, Stefan Lorkowski, Giancarlo Lucchetti, Ricardo Lutzky Saute, Francesca Giulia Magnani, Ahmad Azam Malik, João Massano, Man Mohan Mehndiratta, Ritesh G Menezes, Atte Meretoja, Bahram Mohajer, Norlinah Mohamed Ibrahim, Yousef Mohammad, Arif Mohammed, Ali H Mokdad, Stefania Mondello, Mohammad Ali Moni, Md Moniruzzaman, Tilahun Belete Mossie, Gabriele Nagel, Muhammad Naveed, Vinod C Nayak, Sandhya Neupane Kandel, Trang Huyen Nguyen, Bogdan Oancea, Nikita Otstavnov, Stanislav S Otstavnov, Mayowa O Owolabi, Songhomitra Panda-Jonas, Fatemeh Pashazadeh Kan, Maja Pasovic, Urvish K Patel, Mona Pathak, Mario F P Peres, Arokiasamy Perianayagam, Carrie B Peterson, Michael R Phillips, Marina Pinheiro, Michael A Piradov, Constance Dimity Pond, Michele H Potashman, Faheem Hyder Pottoo, Sergio I Prada, Amir Radfar, Alberto Raggi, Fakher Rahim, Mosiur Rahman, Pradhum Ram, Priyanga Ranasinghe, David Laith Rawaf, Salman Rawaf, Nima Rezaei, Aziz Rezapour, Stephen R Robinson, Michele Romoli, Gholamreza Roshandel, Ramesh Sahathevan, Amirhossein Sahebkar, Mohammad Ali Sahraian, Brijesh Sathian, Davide Sattin, Monika Sawhney, Mete Saylan, Silvia Schiavolin, Allen Seylani, Feng Sha, Masood Ali Shaikh, K S Shaji, Mohammed Shannawaz, Jeevan K Shetty, Mika Shigematsu, Jae Il Shin, Rahman Shiri, Diego Augusto Santos Silva, João Pedro Silva, Renata Silva, Jasvinder A Singh, Valentin Yurievich Skryabin, Anna Aleksandrovna Skryabina, Amanda E Smith, Sergey Soshnikov, Emma Elizabeth Spurlock, Dan J Stein, Jing Sun, Rafael Tabarés-Seisdedos, Bhaskar Thakur, Binod Timalsina, Marcos Roberto Tovani-Palone, Bach Xuan Tran, Gebiyaw Wudie Tsegaye, Sahel Valadan Tahbaz, Pascual R Valdez, Narayanaswamy Venketasubramanian, Vasily Vlassov, Giang Thu Vu, Linh Gia Vu, Yuan-Pang Wang, Anders Wimo, Andrea Sylvia Winkler, Lalit Yadav, Seyed Hossein Yahyazadeh Jabbari, Kazumasa Yamagishi, Lin Yang, Yuichiro Yano, Naohiro Yonemoto, Chuanhua Yu, Ismaeel Yunusa, Siddhesh Zadey, Mikhail Sergeevich Zastrozhin, Anasthasia Zastrozhina, Zhi-Jiang Zhang, Christopher J L Murray, and Theo Vos

Abstract

BACKGROUND: Given the projected trends in population ageing and population growth, the number of people with dementia is expected to increase. In addition, strong evidence has emerged supporting the importance of potentially modifiable risk factors for dementia. Characterising the distribution and magnitude of anticipated growth is crucial for public health planning and resource prioritisation. This study aimed to improve on previous forecasts of dementia prevalence by producing country-level estimates and incorporating information on selected risk factors. METHODS: We forecasted the prevalence of dementia attributable to the three dementia risk factors included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 (high body-mass index, high fasting plasma glucose, and smoking) from 2019 to 2050, using relative risks and forecasted risk factor prevalence to predict GBD risk-attributable prevalence in 2050 globally and by world region and country. Using linear regression models with education included as an additional predictor, we then forecasted the prevalence of dementia not attributable to GBD risks. To assess the relative contribution of future trends in GBD risk factors, education, population growth, and population ageing, we did a decomposition analysis. FINDINGS: We estimated that the number of people with dementia would increase from 57·4 (95% uncertainty interval 50·4-65·1) million cases globally in 2019 to 152·8 (130·8-175·9) million cases in 2050. Despite large increases in the projected number of people living with dementia, age-standardised both-sex prevalence remained stable between 2019 and 2050 (global percentage change of 0·1% [-7·5 to 10·8]). We estimated that there were more women with dementia than men with dementia globally in 2019 (female-to-male ratio of 1·69 [1·64-1·73]), and we expect this pattern to continue to 2050 (female-to-male ratio of 1·67 [1·52-1·85]). There was geographical heterogeneity in the projected inc

Published
2022

32. Human Astrovirus VA1 Encephalitis in Pediatric Patients With Cancer: Report of 2 Cases and Review of the Literature

Author

Sakshi Bami, Jennifer Hidinger, Arshia Madni, Virginia Hargest, Stacey Schultz-Cherry, Valerie Cortez, Shane J Cross, Deborah A Ward, Randall T Hayden, Jeffrey Rubnitz, Ching-Hon Pui, Raja B Khan, and Diego R Hijano

Subjects
Feces, Immunocompromised Host, Infectious Diseases, Astroviridae Infections, Neoplasms, Pediatrics, Perinatology and Child Health, Encephalitis, Humans, General Medicine, Child, Phylogeny, Mamastrovirus
Abstract

Novel human astroviruses (HAstVs) have recently been implicated as rare causes of fatal encephalitis in immunocompromised patients, for which there is no proven treatment. We report 2 cases from our institution in which HAstV-VA1 was detected in the cerebrospinal fluid by metagenomic next-generation sequencing after the initial evaluation revealed no etiology.

Published
2022

33. Applications of nanomaterials for health and environment protection

Author

Asif Iqbal, Muhammad Hassan Raza, Haleema Jadoon, Aleesha Jamshed, Bakht B Khan, Muhammad IK Rehman, Muhammad Noman, and Muhammad Faizan Zafar

Subjects
General Medicine
Abstract

Nanomaterials have been reported to be useful for synthesizing several kinds of nanomaterials which show diverse advantages. The remarkable developments in nanotechnology and the crucial need to develop robust, economic, and green approaches for ecological conservation, this review highlights the promising applications of nanomaterial and provides a comprehensive view on benefits of nanotechnology regarding the healthcare and environmental safety systems. The concentration on nanotechnology in public debates has increased over the last few decades. Nanotechnology is found to be one of the crucial future knowledge. The current study concentrates on the applications of nanotechnology in ecological safety, particularly in the air and water sectors.

Published
2022

34. Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Author

Angelin Blessy, Avneesh Kumar, null Prabagaran A, Abdul Quadir Md, Abdullah I. Alharbi, Ahlam Almusharraf, and Surbhi B. Khan

Subjects
Renewable Energy, Sustainability and the Environment, Geography, Planning and Development, sustainable irrigation system, IoT, LSTM, ANFIS, soil sensing, transfer learning, Building and Construction, Management, Monitoring, Policy and Law
Abstract

Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R2, MSLE and the average initial prediction value of R2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

Published
2023
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35. High-flow oxygen therapy v. standard care in infants with viral bronchiolitis

Author

A B Khan, E Bruckmann, S Omar, A Salloo, S Murphy, and LG Doedens

Subjects
medicine.medical_specialty, Respiratory rate, Respiratory distress, business.industry, medicine.medical_treatment, high-flow oxygen, bronchiolitis, paediatric intensive care, Critical Care and Intensive Care Medicine, medicine.disease, medicine.disease_cause, law.invention, Randomized controlled trial, law, Bronchiolitis, Heart rate, Emergency medicine, Breathing, Medicine, Intubation, business, Nasal cannula
Abstract

High-flow humidified oxygen (HFHO) therapy has demonstrated benefit in infants with bronchiolitis.To investigate the efficacy of HFHO in infants with moderate to severe viral bronchiolitis, when used outside the paediatric intensive care unit (PICU), in a hospital with limited PICU resources.A randomised controlled trial, which enrolled 28 infants between 1 month and 2 years of age, with a clinical diagnosis of acute viral bronchiolitis and moderate to severe respiratory distress. Participants were randomised to receive HFHO 2L/kg/min or oxygen by nasal cannula/ face mask. Respiratory rate, heart rate, oxygen saturations, and modified TAL (M-TAL) score were measured at baseline, 60 - 90 minutes after starting therapy and at 6- and 12-hourly intervals. The primary outcome evaluated was the improvement in respiratory distress (M-TAL score). The secondary outcome assessed was the need for intubation and ventilation.There was a significant improvement in respiratory distress (M-TAL score), in infants who received HFHO therapy. Additionally, there was also a reduction in heart rate in the HFHO group as well as a trend to lower intubation rates.HFHO is a beneficial therapy for infants with moderate-severe viral bronchiolitis. It can be safely used outside the PICU and could potentially reduce the need for intubation and admission to PICU in resource-limited settings.High-flow humidified oxygen (HFHO) is effective in infants with moderate to severe bronchiolitis, and not only in those with milder forms of the disease. It can be safely used outside the paediatric intensive care unit, where adequate respiratory monitoring is available. This is important in low-resource areas where there may be insufficient critical care resources to manage these patients.

Published
2023

36. Data from Approach for Classification and Severity Grading of Long-term and Late-Onset Health Events among Childhood Cancer Survivors in the St. Jude Lifetime Cohort

Author

Leslie L. Robison, Kirsten K. Ness, Matthew Wilson, Mary Ellen Hoehn, Dennis Stokes, Saumini Srinivasan, Vijaya M. Joshi, Yutaka Yasui, Deo Kumar Srivastava, Raja B. Khan, Sue Kaste, Karen Hale, Johnnie K. Bass, I-Chan Huang, Carmen L. Wilson, Noah D. Sabin, Kevin R. Krull, James L. Klosky, Tara M. Brinkman, Gregory T. Armstrong, Daniel A. Mulrooney, Daniel M. Green, Wassim Chemaitilly, Hesham Eissa, Malek Baassiri, Nickhill Bhakta, Matthew J. Ehrhardt, and Melissa M. Hudson

Abstract

Characterization of toxicity associated with cancer and its treatment is essential to quantify risk, inform optimization of therapeutic approaches for newly diagnosed patients, and guide health surveillance recommendations for long-term survivors. The NCI Common Terminology Criteria for Adverse Events (CTCAE) provides a common rubric for grading severity of adverse outcomes in cancer patients that is widely used in clinical trials. The CTCAE has also been used to assess late cancer treatment-related morbidity but is not fully representative of the spectrum of events experienced by pediatric and aging adult survivors of childhood cancer. Also, CTCAE characterization does not routinely integrate detailed patient-reported and medical outcomes data available from clinically assessed cohorts. To address these deficiencies, we standardized the severity grading of long-term and late-onset health events applicable to childhood cancer survivors across their lifespan by modifying the existing CTCAE v4.03 criteria and aligning grading rubrics from other sources for chronic conditions not included or optimally addressed in the CTCAE v4.03. This article describes the methods of late toxicity assessment used in the St. Jude Lifetime Cohort Study, a clinically assessed cohort in which data from multiple diagnostic modalities and patient-reported outcomes are ascertained. Cancer Epidemiol Biomarkers Prev; 26(5); 666–74. ©2016 AACR.

Published
2023

37. Supplemental Table 1 from Approach for Classification and Severity Grading of Long-term and Late-Onset Health Events among Childhood Cancer Survivors in the St. Jude Lifetime Cohort

Author

Leslie L. Robison, Kirsten K. Ness, Matthew Wilson, Mary Ellen Hoehn, Dennis Stokes, Saumini Srinivasan, Vijaya M. Joshi, Yutaka Yasui, Deo Kumar Srivastava, Raja B. Khan, Sue Kaste, Karen Hale, Johnnie K. Bass, I-Chan Huang, Carmen L. Wilson, Noah D. Sabin, Kevin R. Krull, James L. Klosky, Tara M. Brinkman, Gregory T. Armstrong, Daniel A. Mulrooney, Daniel M. Green, Wassim Chemaitilly, Hesham Eissa, Malek Baassiri, Nickhill Bhakta, Matthew J. Ehrhardt, and Melissa M. Hudson

Abstract

Neuropsychological Event Grading in SJLIFE

Published
2023

38. Supplemental Table 2 from Approach for Classification and Severity Grading of Long-term and Late-Onset Health Events among Childhood Cancer Survivors in the St. Jude Lifetime Cohort

Author

Leslie L. Robison, Kirsten K. Ness, Matthew Wilson, Mary Ellen Hoehn, Dennis Stokes, Saumini Srinivasan, Vijaya M. Joshi, Yutaka Yasui, Deo Kumar Srivastava, Raja B. Khan, Sue Kaste, Karen Hale, Johnnie K. Bass, I-Chan Huang, Carmen L. Wilson, Noah D. Sabin, Kevin R. Krull, James L. Klosky, Tara M. Brinkman, Gregory T. Armstrong, Daniel A. Mulrooney, Daniel M. Green, Wassim Chemaitilly, Hesham Eissa, Malek Baassiri, Nickhill Bhakta, Matthew J. Ehrhardt, and Melissa M. Hudson

Abstract

Medical Event Grading in SJLIFE

Published
2023

39. Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years

Author

Ayanda M. Magwenyane, Samuel C. Ugbaja, Daniel G. Amoako, Anou M. Somboro, Rene B. Khan, and Hezekiel M. Kumalo

Subjects
General Immunology and Microbiology, Computers, Neoplasms, Applied Mathematics, Modeling and Simulation, Drug Discovery, Humans, Antineoplastic Agents, HSP90 Heat-Shock Proteins, General Medicine, General Biochemistry, Genetics and Molecular Biology
Abstract

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell’s life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.

Published
2022

40. <scp>CD70</scp> is a potential target biomarker in peripheral T‐cell lymphomas

Author

Mario L Marques‐Piubelli, Jason Sagert, Minh Thu Pham, Matthias Will, Dan Henderson, Kimberly Tipton, Swaminathan Iyer, Wei Lu, Khaja B Khan, Leticia Hamana, Jennifer R Chapman, Beenu Thakral, Jie Xu, Roberto N Miranda, Kristofer Jennings, Luisa M Solis, and Francisco Vega

Subjects
Histology, Humans, Lymphoma, T-Cell, Peripheral, General Medicine, Lymphocyte Activation, Biomarkers, CD27 Ligand, Pathology and Forensic Medicine
Published
2022

41. The Stroke Preclinical Assessment Network: Rationale, Design, Feasibility, and Stage 1 Results

Author

Patrick D. Lyden, Francesca Bosetti, Márcio A. Diniz, André Rogatko, James I. Koenig, Jessica Lamb, Karisma A. Nagarkatti, Ryan P. Cabeen, David C. Hess, Pradip K. Kamat, Mohammad B. Khan, Kristofer Wood, Krishnan Dhandapani, Ali S. Arbab, Enrique C. Leira, Anil K. Chauhan, Nirav Dhanesha, Rakesh B. Patel, Mariia Kumskova, Daniel Thedens, Andreia Morais, Takahiko Imai, Tao Qin, Cenk Ayata, Ligia S.B. Boisserand, Alison L. Herman, Hannah E. Beatty, Sofia E. Velazquez, Sebastian Diaz-Perez, Basavaraju G. Sanganahalli, Jelena M. Mihailovic, Fahmeed Hyder, Lauren H. Sansing, Raymond C. Koehler, Steven Lannon, Yanrong Shi, Senthilkumar S. Karuppagounder, Adnan Bibic, Kazi Akhter, Jaroslaw Aronowski, Louise D. McCullough, Anjali Chauhan, Andrew Goh, Shahneela Siddiqui, Kevin Sheth, Charles Matouk, Charles Dela Cruz, Jiangbing Zhou, Valina L. Dawson, Ted M. Dawson, Jian Liang, Peter C.M. van Zijl, Steven R. Zeiler, W. Taylor Kimberly, Taylan Erdogan, Lili Yu, Joseph Mandeville, and Jonah Patrick Weigand Whittier

Subjects
Male, Advanced and Specialized Nursing, Brain, Infarction, Middle Cerebral Artery, Article, Brain Ischemia, Stroke, Mice, Animals, Feasibility Studies, Humans, Neurology (clinical), Cardiology and Cardiovascular Medicine, Aged
Abstract

Cerebral ischemia and reperfusion initiate cellular events in brain that lead to neurological disability. Investigating these cellular events provides ample targets for developing new treatments. Despite considerable work, no such therapy has translated into successful stroke treatment. Among other issues—such as incomplete mechanistic knowledge and faulty clinical trial design—a key contributor to prior translational failures may be insufficient scientific rigor during preclinical assessment: nonblinded outcome assessment; missing randomization; inappropriate sample sizes; and preclinical assessments in young male animals that ignore relevant biological variables, such as age, sex, and relevant comorbid diseases. Promising results are rarely replicated in multiple laboratories. We sought to address some of these issues with rigorous assessment of candidate treatments across 6 independent research laboratories. The Stroke Preclinical Assessment Network (SPAN) implements state-of-the-art experimental design to test the hypothesis that rigorous preclinical assessment can successfully reduce or eliminate common sources of bias in choosing treatments for evaluation in clinical studies. SPAN is a randomized, placebo-controlled, blinded, multilaboratory trial using a multi-arm multi-stage protocol to select one or more putative stroke treatments with an implied high likelihood of success in human clinical stroke trials. The first stage of SPAN implemented procedural standardization and experimental rigor. All participating research laboratories performed middle cerebral artery occlusion surgery adhering to a common protocol and rapidly enrolled 913 mice in the first of 4 planned stages with excellent protocol adherence, remarkable data completion and low rates of subject loss. SPAN stage 1 successfully implemented treatment masking, randomization, prerandomization inclusion/exclusion criteria, and blinded assessment to exclude bias. Our data suggest that a large, multilaboratory, preclinical assessment effort to reduce known sources of bias is feasible and practical. Subsequent SPAN stages will evaluate candidate treatments for potential success in future stroke clinical trials using aged animals and animals with comorbid conditions.

Published
2022

42. The posterior fossa syndrome questionnaire: using science to inform practice

Author

Molly E. Wickenhauser, Raja B. Khan, Darcy Raches, Jason M. Ashford, Kathryn M. W. Russell, Kristin Lyons, Giles W. Robinson, Amar Gajjar, Paul Klimo, and Heather M. Conklin

Subjects
Adult, Male, Cancer Research, Adolescent, Cerebellar Ataxia, Mutism, Article, Young Adult, Postoperative Complications, Neurology, Oncology, Child, Preschool, Surveys and Questionnaires, Humans, Ataxia, Female, Neurology (clinical), Cerebellar Neoplasms, Child, Medulloblastoma
Abstract

INTRODUCTION: Up to 34% of patients with medulloblastoma develop posterior fossa syndrome (PFS) following brain tumor resection and have increased risk of long-term neurocognitive impairments. Lack of agreement in conceptualization and diagnosis of PFS calls for improvements in diagnostic methods. The current study aimed to describe psychometric properties of a new Posterior Fossa Syndrome Questionnaire (PFSQ). METHODS: The PFSQ was informed by prior research and developed by a multidisciplinary team with subject matter expertise. Participants (N = 164; 63.4% Male; 78.7% White; M(age at diagnosis) = 10.38 years, SD = 5.09, range 3 – 31 years) included patients with newly diagnosed medulloblastoma enrolled in the SJMB12 clinical trial. Forty-four patients (26.8%) were classified as having PFS based on attending physician’s post-surgical yes/no report. A PFSQ was completed by a neurologist within 2 weeks of coming to St. Jude Children’s Research Hospital for adjuvant treatment, irrespective of suspicion for PFS. RESULTS: PFSQ items Ataxia (100.00%), Dysmetria (95.45%), and Speech/Language Changes (79.55%) were most sensitive. However, Ataxia (26.50%) and Dysmetria (46.61%) demonstrated low specificity. Speech/Language Changes (81.36%), Mutism (95.76%), Orofacial Apraxia (98.29%) and Irritability (96.61%) had high specificity. A principal component analysis found four components: 1) Speech/Language Changes, 2) Apraxias (including mutism), 3) Motor/Oromotor, and 4) Emotional Lability. CONCLUSIONS: The PFSQ is a dimensional diagnostic approach that can be used to improve diagnostic consistency across clinical and research groups to help accelerate understanding of PFS etiology, identify surgical correlates of risk, predict long-term impairments, and develop targeted interventions. Additional measure validation, including correlation with symptom resolution, is required.

Published
2022

43. Numerical treatment of time dependent magnetohydrodynamic nanofluid flow of mass and heat transport subject to chemical reaction and heat source

Author

M. Kbiri Alaoui, M. Ijaz Khan, Niaz B. Khan, Yun-Xiang Li, S. Baag, Satyaranjan Mishra, P. K. Pattnaik, Sami Ullah Khan, and Yong-Min Li

Subjects
Volume fraction, Materials science, Natural convection, Diffusion, Prandtl number, General Engineering, Mechanics, Nanofluid, Numerical method, Engineering (General). Civil engineering (General), Lewis number, Radiation absorption, Magnetohydrodynamics, symbols.namesake, Thermal conductivity, Heat transfer, symbols, Magnetohydrodynamic drive, TA1-2040
Abstract

The thermophysical properties of the nanofluids override the physical properties of conventional fluids due to the high thermal conductivity of the nanoparticles. The applications of nanofluids have varied use in the era of bio-engineering and thermal sciences. Moreover, the nanoparticles attributed the significances in human blood arteries. Therefore, the present analysis manifests with the free convection of electrically conducting nanofluid embedding with resistive forces such that the magnetic field and permeability of the medium. The augmentation in the heat transfer properties is obtained because of the inclusion of radiative heat energy and absorption diffusion. Moreover, the nanoparticles volume fraction is focused with implementation of chemical reaction. Numerical treatment using the code in-build MATLAB code pdepeis employed for the transformed unsteady flow problem. The graphical computations against flow parameters are worked with justified physical importance. A declining change in nanoparticles is examined due to Prandtl number which is more progressive for steady case. The decreasing change in velocity is more dominant for nanoparticles as compared to pure fluid. The nanoparticles concentration reduces for effective variation of chemical reaction constant and Lewis number.

Published
2022

44. Does habitat heterogeneity influence taxonomic richness and abundance? A case study from a terrestrial protected area in Abu Dhabi, United Arab Emirates

Author

Imran Khaliq, Ahmed Ali, Robert E. Gubiani, Shaikha Al Dhaheri, Pritpal S. Soorae, Shahid B. Khan, Rashed Al Zaabi, Shakeel Ahmed, Anitha Saji, Salim Javed, Khaldoun Al Omari, Eissa Al Hammadi, and Sabitha Sakkir

Subjects
Geography, Habitat, Abundance (ecology), Ecology, Biodiversity, Land use, land-use change and forestry, Species richness, General Agricultural and Biological Sciences, Protected area, Relative species abundance, Spatial heterogeneity
Abstract

Biodiversity is under enormous pressure from multiple threats including climate change, land use change, habitat alterations and hunting pressure. One way to ease this pressure on biodiversity and to mitigate the effects of above-mentioned threats, is to establish protected areas. Importance of protected increases many folds in regions that are considered as biodiversity poor regions i.e. deserts. Protected areas have long been a major pillar of biodiversity conservation strategies; the Houbara Protected Area (HPA) is one of the 13 terrestrial protected areas in Abu Dhabi Emirate officially declared in 2017. However, no information regarding the status of biodiversity in the HPA has been communicated to the research fraternity. During the present study, surveys were conducted to fill this gap. The survey area was divided in to 50 grids of 5 × 5 km2 and monitoring surveys were undertaken from January to December 2016. A total of 14 bi-monthly to monthly surveys were conducted within HPA and 196 species of different taxonomical groups were recorded. A year-long survey yielded highly diversified fauna and flora from 19 different habitat types (H) 1.32, (E) 2.28, Shannon Diversity Index). We looked at the influence of habitat breadth and temperature on the species richness and abundance, results shows that in desert setup heterogeneity of habitat is not an important factor in maintaining the biodiversity as total number of individuals as well as species were similar in the grids that have different number of habitat types (df = 34.3, t = -0.472, P = 0.640). However, we did find a positive impact of mean monthly temperature on species richness (df = 154, t = 2.53, P = 0.012). Our study highlights the importance of temperature in driving species abundance and richness in protected area. Abundance and species richness are similar in protected areas indicating that protection is allowing species to explore the heterogenous habitats. Overall, we can conclude that protection is beneficial for species.

Published
2022

45. Observation of muonic Dalitz decays of chib mesons and precise spectroscopy of hidden-beauty states

Author

The LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, F. Alessio, M. Alexander, Z. Aliouche, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, A. Andreianov, P. Andreola, M. Andreotti, D. Andreou, A. Anelli, D. Ao, F. Archilli, M. Argenton, S. Arguedas Cuendis, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, W. Baldini, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, M. Bartolini, J. Bartz, J. M. Basels, S. Bashir, G. Bassi, B. Batsukh, P. B. Battista, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. B. Behling, S. Belin, V. Bellee, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, C. Betancourt, F. Betti, J. Bex, Ia. Bezshyiko, J. Bhom, M. S. Bieker, N. V. Biesuz, P. Billoir, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. S. Bolognani, R. Bolzonella, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, A. Brea Rodriguez, N. Breer, J. Brodzicka, A. Brossa Gonzalo, J. Brown, D. Brundu, E. Buchanan, A. Buonaura, L. Buonincontri, A. T. Burke, C. Burr, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, A. C. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, S. Cali, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, D. H. Campora Perez, A. F. Campoverde Quezada, S. Capelli, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, J. Castro Godinez, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, D. Cervenkov, S. Cesare, A. J. Chadwick, I. Chahrour, M. Charles, Ph. Charpentier, E. Chatzianagnostou, M. Chefdeville, C. Chen, S. Chen, Z. Chen, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, S. Cholak, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, P. Collins, T. Colombo, M. C. Colonna, A. Comerma-Montells, L. Congedo, A. Contu, N. Cooke, I. Corredoira, A. Correia, G. Corti, J. J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, L. Dai, X. Dai, E. Dall’Occo, J. Dalseno, C. D’Ambrosio, J. Daniel, A. Danilina, P. d’Argent, A. Davidson, J. E. Davies, A. Davis, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, V. Dedu, S. Dekkers, L. Del Buono, B. Delaney, H.-P. Dembinski, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, W. Duan, P. Duda, M. W. Dudek, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, J. Ellbracht, S. Ely, A. Ene, E. Epple, J. Eschle, S. Esen, T. Evans, F. Fabiano, L. N. Falcao, Y. Fan, B. Fang, L. Fantini, M. Faria, K. Farmer, D. Fazzini, L. Felkowski, M. Feng, M. Feo, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, M. Fontana, L. F. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Fuehring, Y. Fujii, T. Fulghesu, E. Gabriel, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, M. Garau, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, K. G. Garg, L. Garrido, C. Gaspar, R. E. Geertsema, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. G. Ghizzo, Z. Ghorbanimoghaddam, L. Giambastiani, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, P. Gironella Gironell, C. Giugliano, M. A. Giza, E. L. Gkougkousis, F. C. Glaser, V. V. Gligorov, C. Göbel, E. Golobardes, D. Golubkov, A. Golutvin, A. Gomes, S. Gomez Fernandez, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, L. M. Greeven, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, M. Guittiere, V. Guliaeva, P. A. Günther, A.-K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, J. Haimberger, M. Hajheidari, G. H. Hallett, M. M. Halvorsen, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, M. Hartmann, S. Hashmi, J. He, F. Hemmer, C. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Hicheur, G. Hijano Mendizabal, D. Hill, S. E. Hollitt, J. Horswill, R. Hou, Y. Hou, N. Howarth, J. Hu, W. Hu, X. Hu, W. Huang, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Inglessi, A. Iniukhin, A. Ishteev, K. Ivshin, R. Jacobsson, H. Jage, S. J. Jaimes Elles, S. Jakobsen, E. Jans, B. K. Jashal, A. Jawahery, V. Jevtic, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, T. P. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, D. Karpenkov, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, T. Kirn, V. S. Kirsebom, O. Kitouni, S. Klaver, N. Kleijne, K. Klimaszewski, M. R. Kmiec, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, M. Korolev, I. Kostiuk, O. Kot, S. Kotriakhova, A. Kozachuk, P. Kravchenko, L. Kravchuk, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. K. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, B. K. Kutsenko, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, R. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, O. Lantwin, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. Lesser, B. Leverington, A. Li, C. Li, H. Li, K. Li, L. Li, M. Li, P. Li, P.-R. Li, Q. Li, S. Li, T. Li, Y. Li, Z. Lian, X. Liang, S. Libralon, C. Lin, T. Lin, R. Lindner, V. Lisovskyi, R. Litvinov, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. L. Liu, A. Lobo Salvia, A. Loi, J. Lomba Castro, T. Long, J. H. Lopes, A. Lopez Huertas, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, V. Lukashenko, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X.-R. Lyu, G. M. Ma, R. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, A. Maevskiy, D. Magdalinski, D. Maisuzenko, M. W. Majewski, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Santos, F. Martinez Vidal, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, M. McCann, L. Mcconnell, T. H. McGrath, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, G. Meier, D. Melnychuk, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, A. Modak, R. A. Mohammed, R. D. Moise, S. Mokhnenko, E. F. Molina Cardenas, T. Mombächer, M. Monk, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, J. Moron, A. B. Morris, A. G. Morris, R. Mountain, H. Mu, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, I. Nasteva, M. Needham, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, S. Okamura, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, M. Olocco, C. J. G. Onderwater, R. H. O’Neil, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, P. R. Pais, T. Pajero, A. Palano, M. Palutan, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, B. Passalacqua, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, A. Pereiro Castro, P. Perret, A. Perro, K. Petridis, A. Petrolini, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, D. Pinci, F. Pisani, M. Pizzichemi, V. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, G. Punzi, S. Qasim, Q. Q. Qian, W. Qian, N. Qin, S. Qu, R. Quagliani, R. I. Rabadan Trejo, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, R. Ribatti, G. R. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, M. Romero Lamas, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, A. Ryzhikov, J. Ryzka, J. J. Saavedra-Arias, J. J. Saborido Silva, R. Sadek, N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, L. G. Scantlebury Smead, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, S. Schulte, M. H. Schune, R. Schwemmer, G. Schwering, B. Sciascia, A. Sciuccati, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, Q. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, N. Skidmore, T. Skwarnicki, M. W. Slater, J. C. Smallwood, E. Smith, K. Smith, M. Smith, A. Snoch, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, I. Solovyev, R. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, E. Spadaro Norella, E. Spedicato, J. G. Speer, E. Spiridenkov, P. Spradlin, V. Sriskaran, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, E. N. Stein, O. Steinkamp, O. Stenyakin, H. Stevens, D. Strekalina, Y. Su, F. Suljik, J. Sun, L. Sun, Y. Sun, D. Sundfeld, W. Sutcliffe, P. N. Swallow, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, M. D. Tat, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, H. Tilquin, V. Tisserand, S. T’Jampens, M. Tobin, L. Tomassetti, G. Tonani, X. Tong, D. Torres Machado, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, E. Ursov, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, D. Vico Benet, P. V. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, G. Vouters, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, C. Wang, G. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. W. Wang, Y. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, G. Wilkinson, M. K. Wilkinson, M. Williams, M. R. J. Williams, R. Williams, Z. Williams, F. F. Wilson, W. Wislicki, M. Witek, L. Witola, G. Wormser, S. A. Wotton, H. Wu, J. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, A. Xu, J. Xu, L. Xu, M. Xu, Z. Xu, D. Yang, K. Yang, S. Yang, X. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, E. Zaffaroni, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, S. Zhang, Y. Zhang, Y. Z. Zhang, Y. Zhao, A. Zharkova, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, V. Zhukov, J. Zhuo, Q. Zou, D. Zuliani, and G. Zunica

Subjects
Hadron-Hadron Scattering, Quarkonium, Spectroscopy, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract The decays of the χb1(1P), χb2(1P), χb1(2P) and χb2(2P) mesons into the Υ(1S)μ+μ − final state are observed with a high significance using proton-proton collision data collected with the LHCb detector and corresponding to an integrated luminosity of 9 fb −1. The newly observed decays together with the Υ(2S) → Υ(1S)π+π − and Υ(3S) → Υ(2S)π+π − decay modes are used for precision measurements of the mass and mass splittings for the hidden-beauty states.

Published
2024
Full Text
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46. Transverse polarization measurement of Λ hyperons in pNe collisions at s NN $$ \sqrt{{\textrm{s}}_{\textrm{NN}}} $$ = 68.4 GeV with the LHCb detector

Author

The LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, F. Alessio, M. Alexander, Z. Aliouche, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, A. Andreianov, P. Andreola, M. Andreotti, D. Andreou, A. Anelli, D. Ao, F. Archilli, M. Argenton, S. Arguedas Cuendis, A. Artamonov, M. Artuso, E. Aslanides, R. Ataide Da Silva, M. Atzeni, B. Audurier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, W. Baldini, H. Bao, J. Baptista de Souza Leite, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, M. Bartolini, J. Bartz, J. M. Basels, G. Bassi, B. Batsukh, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, S. Belin, V. Bellee, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, C. Betancourt, F. Betti, J. Bex, Ia. Bezshyiko, J. Bhom, M. S. Bieker, N. V. Biesuz, P. Billoir, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. S. Bolognani, R. Bolzonella, N. Bondar, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, S. A. Bouchiba, T. J. V. Bowcock, A. Boyer, C. Bozzi, A. Brea Rodriguez, N. Breer, J. Brodzicka, A. Brossa Gonzalo, J. Brown, D. Brundu, E. Buchanan, A. Buonaura, L. Buonincontri, A. T. Burke, C. Burr, A. Butkevich, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, R. Calabrese, S. Calderon Ramirez, L. Calefice, S. Cali, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, D. H. Campora Perez, A. F. Campoverde Quezada, S. Capelli, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, J. Castro Godinez, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, D. Cervenkov, S. Cesare, A. J. Chadwick, I. Chahrour, M. Charles, Ph. Charpentier, E. Chatzianagnostou, C. A. Chavez Barajas, M. Chefdeville, C. Chen, S. Chen, Z. Chen, A. Chernov, S. Chernyshenko, V. Chobanova, S. Cholak, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, P. Collins, T. Colombo, A. Comerma-Montells, L. Congedo, A. Contu, N. Cooke, I. Corredoira, A. Correia, G. Corti, J. J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, L. Dai, X. Dai, E. Dall’Occo, J. Dalseno, C. D’Ambrosio, J. Daniel, A. Danilina, P. d’Argent, A. Davidson, J. E. Davies, A. Davis, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, V. Dedu, L. Del Buono, B. Delaney, H.-P. Dembinski, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, W. Duan, P. Duda, M. W. Dudek, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, J. Ellbracht, S. Ely, A. Ene, E. Epple, J. Eschle, S. Esen, T. Evans, F. Fabiano, L. N. Falcao, Y. Fan, B. Fang, L. Fantini, M. Faria, K. Farmer, D. Fazzini, L. Felkowski, M. Feng, M. Feo, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, K. M. Fischer, D. S. Fitzgerald, C. Fitzpatrick, F. Fleuret, M. Fontana, L. F. Foreman, R. Forty, D. Foulds-Holt, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Fuehring, Y. Fujii, T. Fulghesu, E. Gabriel, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, Y. Gao, M. Garau, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, K. G. Garg, L. Garrido, C. Gaspar, R. E. Geertsema, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, Z. Ghorbanimoghaddam, L. Giambastiani, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, P. Gironella Gironell, C. Giugliano, M. A. Giza, E. L. Gkougkousis, F. C. Glaser, V. V. Gligorov, C. Göbel, E. Golobardes, D. Golubkov, A. Golutvin, A. Gomes, S. Gomez Fernandez, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. 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Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, M. Mazurek, M. McCann, L. Mcconnell, T. H. McGrath, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, G. Meier, D. Melnychuk, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, A. Modak, A. Mödden, R. A. Mohammed, R. D. Moise, S. Mokhnenko, T. Mombächer, M. Monk, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. B. Morris, A. G. Morris, R. Mountain, H. Mu, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, I. Nasteva, M. Needham, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, P. Nogarolli, P. Nogga, N. S. Nolte, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, S. Okamura, A. Okhotnikov, O. 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Wang, N. W. Wang, R. Wang, X. Wang, X. W. Wang, Y. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, J. Wendel, B. D. C. Westhenry, D. J. White, M. Whitehead, A. R. Wiederhold, D. Wiedner, G. Wilkinson, M. K. Wilkinson, M. Williams, M. R. J. Williams, R. Williams, F. F. Wilson, W. Wislicki, M. Witek, L. Witola, C. P. Wong, G. Wormser, S. A. Wotton, H. Wu, J. Wu, Y. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, A. Xu, J. Xu, L. Xu, M. Xu, Z. Xu, D. Yang, K. Yang, S. Yang, X. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, C. Y. Yu, J. Yu, X. Yuan, E. Zaffaroni, M. Zavertyaev, M. Zdybal, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, S. Zhang, Y. Zhang, Y. Z. Zhang, Y. Zhao, A. Zharkova, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, V. Zhukov, J. Zhuo, Q. Zou, D. Zuliani, and G. Zunica

Subjects
Fixed Target Experiments, Polarization, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract A measurement of the transverse polarization of the Λ and Λ ¯ $$ \overline{\varLambda} $$ hyperons in pNe fixed-target collisions at s NN $$ \sqrt{{\textrm{s}}_{\textrm{NN}}} $$ = 68.4 GeV is presented using data collected by the LHCb detector. The polarization is studied using the decay Λ → pπ − together with its charge conjugated process, the integrated values measured are P Λ = 0.029 ± 0.019 stat ± 0.012 syst , P Λ ¯ = 0.003 ± 0.023 stat ± 0.014 syst . $$ {\displaystyle \begin{array}{c}{P}_{\varLambda }=0.029\pm 0.019\left(\textrm{stat}\right)\pm 0.012\left(\textrm{syst}\right),\\ {}{P}_{\overline{\varLambda}}=0.003\pm 0.023\left(\textrm{stat}\right)\pm 0.014\left(\textrm{syst}\right).\end{array}} $$ Furthermore, the results are shown as a function of the Feynman x variable, transverse momentum, pseudorapidity and rapidity of the hyperons, and are compared with previous measurements.

Published
2024
Full Text
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47. Comprehensive analysis of local and nonlocal amplitudes in the B 0 → K *0 μ + μ − decay

Author

The LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, F. Alessio, M. Alexander, Z. Aliouche, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, A. Andreianov, P. Andreola, M. Andreotti, D. Andreou, A. Anelli, D. Ao, F. Archilli, M. Argenton, S. Arguedas Cuendis, A. Artamonov, M. Artuso, E. Aslanides, R. Ataide Da Silva, M. Atzeni, B. Audurier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, W. Baldini, H. Bao, J. Baptista de Souza Leite, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, M. Bartolini, J. Bartz, J. M. Basels, G. Bassi, B. Batsukh, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, S. Belin, V. Bellee, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, C. Betancourt, F. Betti, J. Bex, Ia. Bezshyiko, J. Bhom, M. S. Bieker, N. V. Biesuz, P. Billoir, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. S. Bolognani, R. Bolzonella, N. Bondar, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, S. A. Bouchiba, T. J. V. Bowcock, A. Boyer, C. Bozzi, A. Brea Rodriguez, N. Breer, J. Brodzicka, A. Brossa Gonzalo, J. Brown, D. Brundu, E. Buchanan, A. Buonaura, L. Buonincontri, A. T. Burke, C. Burr, A. Butkevich, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, R. Calabrese, S. Calderon Ramirez, L. Calefice, S. Cali, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, D. H. Campora Perez, A. F. Campoverde Quezada, S. Capelli, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, J. Castro Godinez, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, D. Cervenkov, S. Cesare, A. J. Chadwick, I. Chahrour, M. Charles, Ph. Charpentier, E. Chatzianagnostou, C. A. Chavez Barajas, M. Chefdeville, C. Chen, S. Chen, Z. Chen, A. Chernov, S. Chernyshenko, V. Chobanova, S. Cholak, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, P. Collins, T. Colombo, A. Comerma-Montells, L. Congedo, A. Contu, N. Cooke, I. Corredoira, A. Correia, G. Corti, J. J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, L. Dai, X. Dai, E. Dall’Occo, J. Dalseno, C. D’Ambrosio, J. Daniel, A. Danilina, P. d’Argent, A. Davidson, J. E. Davies, A. Davis, O. 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Subjects
B Physics, Flavour Changing Neutral Currents, Flavour Physics, Hadron-Hadron Scattering, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract A comprehensive study of the local and nonlocal amplitudes contributing to the decay B 0 → K *0(→ K + π − )μ + μ − is performed by analysing the phase-space distribution of the decay products. The analysis is based on pp collision data corresponding to an integrated luminosity of 8.4 fb −1 collected by the LHCb experiment. This measurement employs for the first time a model of both one-particle and two-particle nonlocal amplitudes, and utilises the complete dimuon mass spectrum without any veto regions around the narrow charmonium resonances. In this way it is possible to explicitly isolate the local and nonlocal contributions and capture the interference between them. The results show that interference with nonlocal contributions, although larger than predicted, only has a minor impact on the Wilson Coefficients determined from the fit to the data. For the local contributions, the Wilson Coefficient C 9 $$ {\mathcal{C}}_9 $$ , responsible for vector dimuon currents, exhibits a 2.1σ deviation from the Standard Model expectation. The Wilson Coefficients C 10 $$ {\mathcal{C}}_{10} $$ , C 9 ′ $$ {\mathcal{C}}_9^{\prime } $$ and C 10 ′ $$ {\mathcal{C}}_{10}^{\prime } $$ are all in better agreement than C 9 $$ {\mathcal{C}}_9 $$ with the Standard Model and the global significance is at the level of 1.5σ. The model used also accounts for nonlocal contributions from B 0 → K *0[τ + τ − → μ + μ − ] rescattering, resulting in the first direct measurement of the bsττ vector effective-coupling C 9 τ $$ {\mathcal{C}}_{9\tau } $$ .

Published
2024
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49. Exploring the Approaches to Data Flow Computing

Author

Mohammad B. Khan, Abdul R. Khan, and Hasan Alkahtani

Subjects
Biomaterials, Mechanics of Materials, Modeling and Simulation, Electrical and Electronic Engineering, Computer Science Applications
Published
2022

50. Abstract WP226: Circadian Rhythm Influences Immune Response In Ischemic Stroke Mice

Author

Pradip K Kamat, Mohammad B Khan, Babak Baban, Shahneela Siddiqui, and David C Hess

Subjects
Advanced and Specialized Nursing, Neurology (clinical), Cardiology and Cardiovascular Medicine
Abstract

Background: Stroke leads to disability and death worldwide. There is evidence that stroke affects the immune system function, and the clock gene controls the immune system. Stroke elevates the inflammatory cascade. Immune response controls the stroke pathology. However, it is unclear if the circadian rhythm influences the immune system in ischemic stroke mice and affects stroke outcomes. Hypothesis: We hypothesized that immune response might be affected by a circadian rhythm that aggravates stroke pathology in a mouse suture occlusion model Methods: Seven to eight-month-old C57BL/6J (Wild Type, n=8-10 mice/group) mice were randomly assigned to do stroke at the different time points of the day following zeitgeber time at ZT0, ZT6, ZT12, and Z18. Cerebral Ischemia was induced by occlusion of the middle cerebral artery (MCAO) for 60 min. Whole blood was analyzed using flow cytometry, and we observed macrophages (M1, M2), neutrophils (N1, N2), Anti-inflammatory IL10, and pro-inflammatory TNF-α cytokines at 24h and 48h. Forty-eight hours after stroke, TTC staining was done to estimate brain infarction, and the infarct area was measured using NIH-Image J software. Results: There was a significant increase ( p p p p p Conclusion: This study demonstrates that mice brain infarcts are influenced by immune responses that aggravate stroke pathology during their sleep period (noon/ZT6) than during their awake period (midnight/ZT18).

Published
2023

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